1. Field of the Invention
The field of the invention relates to methods of feature extraction, such as edge detection. It can be used in computer vision systems, including image/facial/object detection/recognition systems, scene interpretation, classification and captioning systems.
2. Technical Background
Most of the existing object detection algorithms are based on machine learning classifiers, which in their turn use features extracted from an image. Fundamentally there are two approaches to enhance the results of an object detection algorithm. The first approach is an enhancement of classification methodology, where many techniques have been proposed in the literature (Linear classifiers, Neural networks etc.). The second approach is an enhancement of features used. Researches who focus their work on the enhancement of features extracted from an image mostly concentrate on finding the set of discrete primitives describing the image content. The process of feature extraction is usually related to filtering of the image data and normalisation of the filter's response. However, there is one common flaw in most feature extraction techniques, i.e., during the normalisation and accumulation of image features the assumption is made that the filters producing a stronger response represents stronger image features. In practice, research is often being carried out with digital video or photographic images that are products of image processing pipelines, processing the image sensor data with unknown settings. As previously discussed, such processing can significantly alter image data, breaking linear dependencies between parts of an image and unbalancing the appearance of different image elements.
This invention provides a solution for a more robust edge detection method by taking sensor characteristics into account during edge detection. The method may be used for feature extraction or feature detection.
3. Discussion of Related Art
The research being conducted at present in the object detection and classification area is very intense. There are a number of object detection techniques, among which HOG-SVM and CNN are widely used.
One of the most successful object detection techniques is known as Histogram of Oriented Gradients—Support Vector Machine (HOG-SVM) as described in [1-5]. The results produced by object detection algorithms are continuously improving. The first step in the calculation of Histogram of Oriented Gradients is edge detection. Standard approaches are presented in [6-10].
A Convolutional Neural Network (CNN) is a type of feed-forward artificial neural network (ANN) where the individual neurons are tiled in such a way that they respond to overlapping regions in the visual field. When used for image recognition, convolutional neural networks (CNNs) consist of multiple layers of small neuron collections which look at small portions of the input image, called receptive fields. The results of these collections are then tiled so that they overlap to obtain a better representation of the original image; this is repeated for every such layer. The layers form a hierarchical system in which the first layers look for lower level features; this is accomplished by means of convolution between a filter and an image.
Existing approaches that assume the object detection algorithm will run on recorded video or still image present a number of issues. First, object detection always needs image processing system to produce a quality RGB image or video sequence, which in many cases means increased system complexity. Secondly the object detection algorithms assume no knowledge about image source, as the image processing settings are not known. Therefore the performance of object detection algorithms may deteriorate quickly in low light conditions.